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Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

  • Define a Function that Makes a Graph
  • Question 1: Use yfinance to Extract Stock Data
  • Question 2: Use Webscraping to Extract Tesla Revenue Data
  • Question 3: Use yfinance to Extract Stock Data
  • Question 4: Use Webscraping to Extract GME Revenue Data
  • Question 5: Plot Tesla Stock Graph
  • Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

Note:- If you are working Locally using anaconda, please uncomment the following code and execute it. Use the version as per your python version.

In [18]:
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly

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In [19]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [20]:
import plotly.io as pio
pio.renderers.default = "iframe"

In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.

In [21]:
import warnings

warnings.filterwarnings("ignore", category = FutureWarning)

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [22]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
    from IPython.display import display, HTML
    fig_html = fig.to_html()
    display(HTML(fig_html))

Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.

Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.

Question 1: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [23]:
import yfinance as yf
tesla = yf.Ticker("TSLA")

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to "max" so we get information for the maximum amount of time.

In [24]:
tesla_data = tesla.history(period ="max")

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [25]:
tesla_data.reset_index(inplace = True)
tesla_data.head()
Out[25]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.

In [26]:
import requests

url = " https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
response = requests.get(url)
html_data = response.text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

In [27]:
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_data, 'html.parser')

Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Step-by-step instructions 

Here are the step-by-step instructions:

1. Create an Empty DataFrame
2. Find the Relevant Table
3. Check for the Tesla Quarterly Revenue Table
4. Iterate Through Rows in the Table Body
5. Extract Data from Columns
6. Append Data to the DataFrame
Click here if you need help locating the table 
    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1

We are focusing on quarterly revenue in the lab.
In [28]:
!pip install pandas
import pandas as pd
from bs4 import BeautifulSoup
import requests

# 1: Create an Empty DataFrame
tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"])

# 2: Fetch the HTML content
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
response = requests.get(url)
html_data = response.text

# 3: Parse HTML with BeautifulSoup
soup = BeautifulSoup(html_data, 'html.parser')

# 4: Find the Relevant Table
table = soup.find_all("tbody")[1]  # Assuming the table is at index 1

# 5: Iterate Through Rows in the Table Body
rows = []
for row in table.find_all("tr"):
    # Step 6: Extract Data from Columns
    columns = row.find_all("td")
    if len(columns) > 1:
        date = columns[0].text.strip()
        revenue = columns[1].text.strip().replace(',', '').replace('$', '')
        
        # Collect data in a list
        rows.append({"Date": date, "Revenue": revenue})

# 7: Convert list to DataFrame
tesla_revenue = pd.DataFrame(rows)

# Display the DataFrame
print(tesla_revenue)

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          Date Revenue
0   2022-09-30   21454
1   2022-06-30   16934
2   2022-03-31   18756
3   2021-12-31   17719
4   2021-09-30   13757
5   2021-06-30   11958
6   2021-03-31   10389
7   2020-12-31   10744
8   2020-09-30    8771
9   2020-06-30    6036
10  2020-03-31    5985
11  2019-12-31    7384
12  2019-09-30    6303
13  2019-06-30    6350
14  2019-03-31    4541
15  2018-12-31    7226
16  2018-09-30    6824
17  2018-06-30    4002
18  2018-03-31    3409
19  2017-12-31    3288
20  2017-09-30    2985
21  2017-06-30    2790
22  2017-03-31    2696
23  2016-12-31    2285
24  2016-09-30    2298
25  2016-06-30    1270
26  2016-03-31    1147
27  2015-12-31    1214
28  2015-09-30     937
29  2015-06-30     955
30  2015-03-31     940
31  2014-12-31     957
32  2014-09-30     852
33  2014-06-30     769
34  2014-03-31     621
35  2013-12-31     615
36  2013-09-30     431
37  2013-06-30     405
38  2013-03-31     562
39  2012-12-31     306
40  2012-09-30      50
41  2012-06-30      27
42  2012-03-31      30
43  2011-12-31      39
44  2011-09-30      58
45  2011-06-30      58
46  2011-03-31      49
47  2010-12-31      36
48  2010-09-30      31
49  2010-06-30      28
50  2010-03-31      21
51  2009-12-31        
52  2009-09-30      46
53  2009-06-30      27

Execute the following line to remove the comma and dollar sign from the Revenue column.

In [29]:
tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace(',', '').str.replace('$', '')

Execute the following lines to remove an null or empty strings in the Revenue column.

In [30]:
# Remove null values
tesla_revenue.dropna(inplace=True)

# Remove empty strings
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [31]:
print(tesla_revenue.tail())

          Date Revenue
48  2010-09-30      31
49  2010-06-30      28
50  2010-03-31      21
52  2009-09-30      46
53  2009-06-30      27

Question 3: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [32]:
!pip install yfinance

import yfinance as yf

gme = yf.Ticker("GME")

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Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to "max" so we get information for the maximum amount of time.

In [34]:
gme_data = gme.history(period = "max")
print(gme_data)

                                Open       High        Low      Close  \
Date                                                                    
2002-02-13 00:00:00-05:00   1.620128   1.693350   1.603296   1.691667   
2002-02-14 00:00:00-05:00   1.712707   1.716074   1.670626   1.683250   
2002-02-15 00:00:00-05:00   1.683251   1.687459   1.658002   1.674834   
2002-02-19 00:00:00-05:00   1.666418   1.666418   1.578047   1.607504   
2002-02-20 00:00:00-05:00   1.615920   1.662210   1.603296   1.662210   
...                              ...        ...        ...        ...   
2025-05-23 00:00:00-04:00  30.600000  33.209999  30.549999  33.029999   
2025-05-27 00:00:00-04:00  33.970001  35.740002  33.630001  35.009998   
2025-05-28 00:00:00-04:00  35.779999  35.810001  30.730000  31.209999   
2025-05-29 00:00:00-04:00  31.170000  31.350000  29.320000  29.570000   
2025-05-30 00:00:00-04:00  29.200001  30.490000  29.190001  29.799999   

                             Volume  Dividends  Stock Splits  
Date                                                          
2002-02-13 00:00:00-05:00  76216000        0.0           0.0  
2002-02-14 00:00:00-05:00  11021600        0.0           0.0  
2002-02-15 00:00:00-05:00   8389600        0.0           0.0  
2002-02-19 00:00:00-05:00   7410400        0.0           0.0  
2002-02-20 00:00:00-05:00   6892800        0.0           0.0  
...                             ...        ...           ...  
2025-05-23 00:00:00-04:00  30477600        0.0           0.0  
2025-05-27 00:00:00-04:00  33217800        0.0           0.0  
2025-05-28 00:00:00-04:00  45300400        0.0           0.0  
2025-05-29 00:00:00-04:00  15819000        0.0           0.0  
2025-05-30 00:00:00-04:00  10189100        0.0           0.0  

[5862 rows x 7 columns]

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [35]:
gme_data.reset_index(inplace = True)
gme_data.head()
Out[35]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683251 1.687459 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data_2.

In [40]:
import requests
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
response = requests.get(url)
html_data2 = response.text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

In [41]:
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_data2, 'html.parser')

Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.

Note: Use the method similar to what you did in question 2.

Click here if you need help locating the table 
    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1
In [42]:
gme_revenue = []

# Find the second table body (index 1)
table = soup.find_all("tbody")[1]
rows = table.find_all("tr")

# Loop through rows and extract data
for row in rows:
    cols = row.find_all("td")
    if len(cols) == 2:
        date = cols[0].text.strip()
        revenue = cols[1].text.strip().replace('$', '').replace(',', '')
        if revenue != '':
            gme_revenue.append({'Date': date, 'Revenue': revenue})

# Convert list to DataFrame
gme_revenue = pd.DataFrame(gme_revenue)

# Optional: Convert Revenue to float
gme_revenue['Revenue'] = gme_revenue['Revenue'].astype(float)

# Display
gme_revenue.head()
Out[42]:
Date Revenue
0 2020-04-30 1021.0
1 2020-01-31 2194.0
2 2019-10-31 1439.0
3 2019-07-31 1286.0
4 2019-04-30 1548.0

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [43]:
gme_revenue.tail()
Out[43]:
Date Revenue
57 2006-01-31 1667.0
58 2005-10-31 534.0
59 2005-07-31 416.0
60 2005-04-30 475.0
61 2005-01-31 709.0

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.

Hint 

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`.
In [38]:
make_graph(tesla_data, tesla_revenue, 'Tesla')

/tmp/ipykernel_302/109047474.py:5: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

/tmp/ipykernel_302/109047474.py:6: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

Hint 

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`
In [44]:
make_graph(gme_data, gme_revenue, 'GameStop') 

/tmp/ipykernel_302/109047474.py:5: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

/tmp/ipykernel_302/109047474.py:6: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log¶

Date (YYYY-MM-DD) Version Changed By Change Description
2022-02-28 1.2 Lakshmi Holla Changed the URL of GameStop
2020-11-10 1.1 Malika Singla Deleted the Optional part
2020-08-27 1.0 Malika Singla Added lab to GitLab

© IBM Corporation 2020. All rights reserved.

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